import pandas as pd
import os
import numpy as np
#import pandas_profiling

path = 'DataSet/Original'
file = 'demographics.csv'

demographics = pd.read_csv(path + '/' + file, sep = ',', doublequote = True)

demographics.shape

(188, 33)

demographics.head()

demographics.borough.value_counts()

Queens           56
Brooklyn         50
Bronx            36
Manhattan        28
Staten Island    18
Name: borough, dtype: int64

demographics['borough'] = demographics['borough'].astype('category')

demographics.dtypes

nta_name              object
borough             category
nta_code              object
population             int64
under_5_years          int64
5-9_years              int64
10-14_years            int64
15-19_years            int64
20-24_years            int64
25-29_years            int64
30-34_years            int64
35-39_years            int64
40-44_years            int64
45-49_years            int64
50-54_years            int64
55-59_years            int64
60-64_years            int64
over_65_years          int64
median_age             int64
people_per_acre      float64
households             int64
less_than_10,000       int64
10000_to_14999         int64
15000_to_24999         int64
25000_to_34999         int64
35000_to_49999         int64
50000_to_74999         int64
75000_to_99999         int64
100000_to_149999       int64
150000_to_199999       int64
200000_or_more         int64
median_income          int64
mean_income            int64
dtype: object

demographics.profile_report()

demographics.describe()

demographics.isna().sum()

nta_name            0
borough             0
nta_code            0
population          0
under_5_years       0
5-9_years           0
10-14_years         0
15-19_years         0
20-24_years         0
25-29_years         0
30-34_years         0
35-39_years         0
40-44_years         0
45-49_years         0
50-54_years         0
55-59_years         0
60-64_years         0
over_65_years       0
median_age          0
people_per_acre     0
households          0
less_than_10,000    0
10000_to_14999      0
15000_to_24999      0
25000_to_34999      0
35000_to_49999      0
50000_to_74999      0
75000_to_99999      0
100000_to_149999    0
150000_to_199999    0
200000_or_more      0
median_income       0
mean_income         0
dtype: int64

	nta_name	borough	nta_code	population	under_5_years	5-9_years	10-14_years	15-19_years	20-24_years	25-29_years	...	15000_to_24999	25000_to_34999	35000_to_49999	50000_to_74999	75000_to_99999	100000_to_149999	150000_to_199999	200000_or_more	median_income	mean_income
0	Allerton-Pelham Gardens	Bronx	BX31	28903	1679	1706	1763	2039	1964	1703	...	797	773	1160	1764	1155	1562	765	427	61638	78489
1	Annadale-Huguenot-Prince's Bay-Eltingville	Staten Island	SI01	27770	1397	1698	1817	1880	1720	1594	...	571	405	1008	1523	1346	2075	1086	1151	88288	109187
2	Arden Heights	Staten Island	SI48	25238	1507	1540	1596	1752	1614	1561	...	337	516	707	1421	1611	2021	1047	740	89570	101627
3	Astoria	Queens	QN70	78793	3480	3037	3060	3392	6630	11586	...	3673	2816	4725	6463	4557	4698	1627	1197	54882	70094
4	Auburndale	Queens	QN48	19996	917	966	1063	1168	1214	1307	...	445	632	690	1417	1060	1237	589	433	70772	84402

	population	under_5_years	5-9_years	10-14_years	15-19_years	20-24_years	25-29_years	30-34_years	35-39_years	40-44_years	...	15000_to_24999	25000_to_34999	35000_to_49999	50000_to_74999	75000_to_99999	100000_to_149999	150000_to_199999	200000_or_more	median_income	mean_income
count	188.000000	188.00000	188.000000	188.000000	188.000000	188.000000	188.000000	188.000000	188.000000	188.000000	...	188.000000	188.000000	188.000000	188.000000	188.000000	188.000000	188.000000	188.000000	188.000000	188.000000
mean	43397.175532	2752.12766	2515.478723	2489.159574	2842.632979	3404.617021	3872.106383	3512.468085	3116.265957	3007.898936	...	1750.861702	1487.734043	1909.824468	2544.452128	1803.074468	2078.186170	939.824468	1224.664894	57002.489362	77808.877660
std	21288.062949	1695.80947	1496.808314	1459.440306	1574.371410	1886.482082	2462.683806	2113.358863	1704.685137	1531.520696	...	1065.437156	893.651778	1076.642531	1415.221055	1041.592900	1430.129096	890.562915	2315.006940	24341.286390	38791.512605
min	13354.000000	506.00000	408.000000	326.000000	449.000000	798.000000	736.000000	742.000000	689.000000	743.000000	...	191.000000	371.000000	496.000000	782.000000	241.000000	201.000000	20.000000	8.000000	20334.000000	29720.000000
25%	27237.000000	1490.50000	1443.500000	1437.750000	1619.000000	1929.500000	2089.250000	1928.000000	1835.750000	1867.500000	...	930.000000	827.750000	1139.500000	1504.750000	1111.000000	1220.250000	426.500000	248.750000	39015.000000	54656.750000
50%	37897.000000	2386.00000	2228.500000	2197.500000	2568.000000	2956.000000	3081.500000	2893.000000	2555.500000	2520.000000	...	1504.500000	1209.000000	1629.000000	2134.000000	1580.000000	1819.500000	760.000000	517.500000	53114.500000	70396.000000
75%	54244.750000	3579.25000	3242.750000	3263.750000	3717.250000	4527.000000	5252.750000	4611.000000	4034.500000	3778.500000	...	2235.250000	2066.500000	2494.250000	3117.500000	2328.500000	2462.500000	1068.750000	963.000000	71430.750000	89989.500000
max	132378.000000	14703.00000	11971.000000	10024.000000	9094.000000	10046.000000	11971.000000	11292.000000	10406.000000	10004.000000	...	5231.000000	4549.000000	5901.000000	7655.000000	6109.000000	9962.000000	6359.000000	15031.000000	155213.000000	311109.000000